Collision avoidance for robots and vehicles in unpredictable environments is a challenging task. Various control strategies have been developed for the agent (i.e., robots or vehicles) to sense the environment, assess the situation, and select the optimal actions to avoid collision and accomplish its mission. In our research on autonomous ships, we take a machine learning approach to collision avoidance. The lack of available ship steering data of human ship masters has made it necessary to acquire collision avoidance knowledge through reinforcement learning (RL). Given that the learned neural network tends to be a black box, it is desirable that a method is available which can be used to design an agent's behavior so that the desired knowledge can be captured. Furthermore, RL with complex tasks can be either time consuming or unfeasible. A multi-stage learning method is needed in which agents can learn from simple tasks and then transfer their learned knowledge to closely related but more complex tasks. In this paper, we explore the ways of designing agent behaviors through tuning reward functions and devise a transfer RL method for multi-stage knowledge acquisition. The computer simulation-based agent training results have shown that it is important to understand the roles of each component in a reward function and the various design parameters in transfer RL. The settings of these parameters are all dependent on the complexity of the tasks and the similarities between them.

An artificial intelligence (AI) algorithm to automate symbolic design reformulation is an enduring challenge in design automation. Existing research shows that design tools either require high levels of knowledge engineering or large databases of training cases. To address these limitations, we present a singular value decomposition (SVD) and unsupervised clustering-based method that performs design reformulation by acquiring semantic knowledge from the syntax of design representations. The development of the method was analogically inspired by applications of SVD in statistical natural language processing and digital image processing. We demonstrate our method on an analytically formulated hydraulic cylinder design problem and an aeroengine design problem formulated using a nonanalytic design structure matrix form. Our results show that the method automates various design reformulation tasks on problems of varying sizes from different design domains, stated in analytic and nonanalytic representational forms. The behavior of the method presents observations that cannot be explained by pure symbolic AI approaches, including uncovering patterns of implicit knowledge that are not readily encoded as logical rules, and automating tasks that require the associative transformation of sets of inputs to experiences. As an explanation, we relate the structure and performance of our algorithm with findings in cognitive neuroscience, and present a set of theoretical postulates addressing an alternate perspective on how symbols may interact with each other in experiences to reify semantic knowledge in design representations.

The spatial synthesis problem addressed in this paper is the configuration of rectangles in 2D space, where the sides of the rectangles are parallel to an orthogonal coordinate system. Variables are the locations of the edges of the rectangles and their orientations. Algebraic constraints on these variables define a layout and constitute a constraint satisfaction problem. We give a new O(n2) algorithm for incremental path-consistency, which is applied after adding each algebraic constraint. Problem requirements are formulated as spatial relations between the rectangles, for example, adjacency, minimum distance, and nonoverlap. Spatial relations are expressed by Boolean combinations of the algebraic constraints; called disjunctive constraints. Solutions are generated by backtracking search, which selects a disjunctive constraint and instantiates its disjuncts. The selected disjuncts describe an equivalence class of configurations that is a significantly different solution. This method generates the set of significantly different solutions that satisfy all the requirements. The order of instantiating disjunctive constraints is critical for search efficiency. It is determined dynamically at each search state, using functions of heuristic measures called textures. Textures implement fail-first and prune-early strategies. Extensions to the model, that is, 3D configurations, configurations of nonrectangular shapes, constraint relaxation, optimization, and adding new rectangles during search are discussed.

Concurrent engineering is a systematic approach to the integrated, concurrent design of products and the related processes of manufacturing and support. This approach is intended to cause the developers, from the outset, to consider all elements of the product life cycle from concept through disposal, including quality, cost, schedule, and user requirements. To achieve successful concurrent-engineering design, one needs an integrated framework, a well-organized design team, and adequate design tools. The research on concurrent engineering to date has focused on developing communication infrastructure, design tools, and product data representations. Little attention has been paid to developing tools to address the organizational issues involved in concurrent engineering. The authors’ research on the Virtual Design Team (VDT) attempts to develop a computerized analysis tool to sup-port the systematic design of organization structures for concurrent engineering projects. VDT is a computer simulation system. It takes descriptions of design tasks, actors (i.e., designers and managers), and organization structure as input, and produces predicted historical records of the actors’ design and coordination behavior, project du-ration, cost, and design process quality as output. VDT has been applied to model more than ten realistic engineering projects, and the results are qualitatively consistent with the predictions from theory and project managers. The VDT framework for modeling concurrent-engineering teams is described, and examples of VDT applications are presented to demonstrate the effectiveness of the Virtual Design Team approach to modeling the organizational behavior of concurrent design teams.

Surface roughness (SR) is one of the major parameters used to govern the quality of the fused deposition modeling (FDM)-printed products, and the FDM process parameters can be easily regulated in order to obtain a good surface finish. The surface quality of the product produced by the FDM is generally affected by the staircase effect that needs to be managed. Also, the production time (PT) to fabricate the product and volume percentage error (VPE) should be minimized to make the FDM process more efficient. The aim of this paper is to accomplish these three objectives with the use of the parametric optimization technique integrating the artificial neural network (ANN) and the whale optimization algorithm (WOA). The FDM parameters which have been taken into consideration are layer thickness, nozzle temperature, printing speed, and raster width. Experimentation has been conducted on printed samples to examine the impact of the input parameters on SR, VPE, and PT according to Taguchi's L27 orthogonal array. The ANN model has been built up using the experimental data, which was further used as an objective function in the WOA with an aim to minimize output responses. The robustness of the proposed method has been validated on the optimal combinations of FDM process parameters.

The aim of this study was to gain further insight into how analogical reasoning and mental simulation, two cognitive strategies, influence team dynamics in innovative product design. A particular emphasis was placed on exploring the association between these two strategies and team cohesion and team collaboration. Analogies were coded for “analogical distance” (i.e., within domain or between domain) and “analogical purpose” (i.e., problem identification, function finding, solution generation, and explanation). The results indicated that the presence of either analogizing or mental simulation was related to team cohesion and team collaboration, with mental simulation having an especially marked association with team collaboration. Within-domain analogizing was found to enhance team collaboration, but it did not influence team cohesion. Furthermore, all types of analogical purpose showed a similar association with team cohesion, whereas solution generation and function finding had a stronger association with team collaboration. We propose that analogizing and mental simulations are strategies that serve valuable functions in engendering enhanced cohesion and collaboration, which might be expected to lead to more effective design outcomes, although this remains an empirical question in need of further corroboration.

This paper reports on a study characterizing design processes and the potential of design spaces through measuring the information entropy of empirical data derived from protocol studies. The sequential segments in a protocol analysis can be related to each other by examining their semantic content producing a design session's linkograph, which defines the design space for a design session. From a linkograph, it is possible to compute the probabilities of the connectivity of each segment for its forelinks and its backlinks, together with the probabilities of distance among links. A linkograph's entropy is a measure of the information in the design session. It is claimed that the entropy of the linkograph measures the potential of the design space being generated as the design proceeds chronologically. We present an approach to the automated construction of linkographs by connecting segments using the lexical database WordNet and measure its entropy. A case study of two design sessions with different characteristics was conducted, one considered more productive and creative, the other more pragmatic. Those segments with high entropy and those associated with high rates of change of entropy are analyzed. The creative session has a higher linkograph entropy. This result indicates the potential of using entropy to characterize a design process.

Four bar mechanisms are basic components of many important mechanical devices. The kinematic synthesis of four bar mechanisms is a difficult design problem. A novel method that combines the genetic programming and decision tree learning methods is presented. We give a structural description for the class of mechanisms that produce desired coupler curves. Constructive induction is used to find and characterize feasible regions of the design space. Decision trees constitute the learning engine, and the new features are created by genetic programming.

The paper provides a solution to the current problem of adaptation in case-based design. The aim of this project is to develop a case-based model of design, using an evolutionary approach, for the adaptation of previously stored design solutions. It is argued that such a knowledge-lean methodology is more general in its applicability than conventional case-based design. A prototypical example in the context of 2-D spatial design of houses is used to test the efficacy and efficiency of this approach.

The concept of shape grammars has often been proposed to improve or support creative design processes. Shape grammar implementations have the potential to both automate parts of the design process and allow exploration of design alternatives. In many of the existing implementations, the main focus is either on capturing the rationale of a particular existing grammar or on allowing designers to develop a new grammar. However, little attention is typically given to the actual representation of the design space that can be explored in the interface of the implementation. With such representation, a shape grammar implementation could properly support designers who are still in the process of designing and may not yet have a clear shape grammar in mind. In this article, an approach and a proof-of-concept software system is proposed for a shape grammar implementation that provides a visual and interactive way to support design space exploration in a creative design process. We describe the method by which this software system can be used and focus on how designers can interact with the exploration process. In particular, we point out how the proposed approach realizes several important amplification strategies to support design space exploration.

MarCon (Market-based Constraints) applies market-based control to distributed constraint problems. It offers a new approach to distributing constraint problems that avoids challenges faced by current approaches in some problem domains, and it provides a systematic method for applying markets to a wide array of problems. Constraint agents interact with one another via the variable agents in which they have a common interest, using expressions of their preferences over sets of assignments. Each variable integrates this information from the constraints interested in it and provides feedback that enables the constraints to shrink their sets of assignments until they converge on a solution. MarCon originated in a system for supporting human product designers, and its mechanisms are particularly useful for applications integrating human and machine intelligence to explore implicit constraints. MarCon has been tested in the domain of mechanical design, in which its set-narrowing process is particularly useful.

In this paper, we present the development and application of a technical feasibility model used in preliminary design to determine whether a set of desired product specifications obtained from marketing is feasible in the engineering domain. This model is developed by integrating the capabilities of a multiobjective design problem, a multicriteria design optimization tool, a Pareto frontier gap analyzer, metamodeling methods, and use of the Pareto frontier as a constraint for feasibility assessment. Although the tools are independent of the domain, their application is illustrated using two examples: a simple three-objective mathematical problem and a five-objective passenger vehicle design problem. The feasibility of the desired product specifications is determined with respect to the problem's Pareto frontier, which is considered to be the necessary constraint to satisfy.

Current design practices mandate that engineering designs be evaluated based on multiple attributes, e.g., cost, power, and area. For multiattribute design problems, generation and evaluation of the Pareto optimal set guarantees the optimal design will be found, but is not practical for a large class of problems. Iterative techniques can be applied to most problems, but sacrifice optimality. In this paper, we introduce a new technique that extends the set of design problems that can be solved optimally. By first constructing an imprecise value function, the number of nondominated alternatives that must be generated is reduced. We describe an implementation based on combinatorial optimization and constraint satisfaction which achieves additional performance gains by decomposing the value function to identify dominated design-variable assignments. Test results indicate that our approach extends the set of problems that can be solved optimally.

Although the case-based reasoning (CBR) process is domain dependent, certain aspects of it can readily be captured into a generic framework which in turn can be applied to various engineering domains. One such exercise that has been carried out is described here. In this paper, we present the notion that CBR can be formalized and applied in a specialized framework in an integrated knowledge-based environment. We first analyze the CBR process to abstract the steps involved in the development of a CBR system. We then propose a framework in which most of these steps are formalized so that they can be applied in a domain-independent manner. The salient features of this framework, called CASETOOL (CASE-based reasoning TOOL-kit), are then described. The highlight of this approach is the use of a concept called design criticism in the CBR process. The versatility of the tool is demonstrated through an application from the bridge engineering domain.

During the course of the design of a complex artifact many thousands of objects will be created that will refer to many different types of information. Thus, an effective design support system must be able to store various types of data and allow easy navigation of the resulting extensive network of objects. In addition, the quality of the design records (both the record of the design artifact and rationale) is increased by the quality and quantity of information which the designer is able to record. In this paper we describe two new developments to Kbds a design support system for chemical process design, which enable easier navigation of the design network, and a fuller representation of the design process. In addition, we show how these extensions may be used together to improve the quality of the design information recorded—both for the evolution of the design artifact and the supporting rationale. The first development enables the designer to record a variety of complementary types of document within the process design history, fulfilling the task of improving the design information recorded. The second enhancement to Kbds eases the navigation of the design history by categorizing design objects according to a user-defined set of keywords. The categorization of design objects is carried out semiautomatically using, in part, the various design representations (documents) enabled through the first extension and enables rapid navigation of the design network. Finally, a method of checking the consistency of design rationale structures using keywords is also described. Thus, various representations may be used to generate keywords that in turn may be used to improve the quality of design rationale records. An example showing this situation is described.

This paper presents a novel knowledge-based Petri net approach to mechanical systems and assemblies modeling within a design with objects environment. A new unified class of object-oriented knowledge Petri nets, which can incorporate a knowledge-based system with ordinary Petri nets, is defined and used for the unified representations of assembly design and modeling. The object knowledge Petri nets, as a graphical language and a new knowledge-based description scheme, can be used to express the qualitative and quantitative aspects of the assembly design and modeling process in an interactive and integrated way. The four-level hierarchy model is proposed and constructed in terms of function-behaviors, structures, geometries, and features. The function-behavior-structure description is built on more abstract concepts so that it can match well top-down design. The static and dynamic characteristics in the design of assembly can also be captured. With the help of fuzzy logic, the incomplete, imprecise knowledge and uncertainty in the design process can also be dealt with. Therefore, the hybrid design object model can incorporate product data model, top-down design process, and assembly process model using an object-oriented, knowledge-based, feature-based, parametric, and constraint-based modeling approach, and can provide a more accurate and more flexible representation. To verify and demonstrate the effective use of the proposed hybrid design object model, a prototype system has been developed. This research provides a knowledge-intensive framework for intelligent assembly design and modeling.

A ubiquitous manufacturing (UM) system is used in manufacturing for obtaining the Internet of things solutions and provides location-based manufacturing services. Human-induced uncertainty and early termination are two complications that hamper the effectiveness of an UM system based on three-dimensional (3D) printing. To resolve these complications, several solutions were considered in this study. First, fuzzy-valued parameters were defined to determine uncertainty. Subsequently, slack was derived to determine whether to restart an early terminated 3D printing process in the same 3D printing facility. Consequently, two optimization models – a fuzzy mixed-integer linear programming model and a fuzzy mixed-integer quadratic programming model – were developed in this study. Based on the two optimization models, a fuzzy 3D printing-based UM system that considers uncertainty and early termination was developed. The effectiveness of the proposed methodology was tested by conducting a regional experiment. The experimental results revealed that the proposed methodology could shorten the average cycle time by 9% and could enable 3D printing facilities to make real-time, online reprinting decisions.

In the present work, a new hybrid approach combining particle swarm optimization (PSO) algorithm with recurrent dynamic neural network (RDNN), which is described as PSO-RDNN algorithm, is proposed for multi-performance optimization of machining parameters in finish turning of hardened AISI D2. The suggested optimization problem is solved using the weighted sum technique. Process parameters including cutting speed and feed rate are optimized for minimizing operation cost, maximizing tool life, and producing parts with acceptable surface roughness. Based on experimental results, two neural network models were developed for predicting tool flank wear and surface roughness during the machining process. Based on trained neural networks and structured hybrid algorithm, optimum cutting parameters were obtained. The coefficient of determination for trained neural networks was calculated as R2 = 0.9893 and R2 = 0.9879 for predicted flank wear and surface roughness, respectively, which proves the efficiency of trained neural models in real industrial applications. Furthermore, the offered methodology returns a Pareto optimality graph, which represents optimized cutting variables for several various cutting conditions.

The goal of machining scheme selection (MSS) is to select the most appropriate machining scheme for a previously designed part, for which the decision maker must take several aspects into consideration. Because many of these aspects may be conflicting, such as time, cost, quality, profit, resource utilization, and so on, the problem is rendered as a multiobjective one. Consequently, we consider a multiobjective optimization problem of MSS in this study, where production profit and machining quality are to be maximized while production cost and production time must be minimized, simultaneously. This paper presents a new discrete method for particle swarm optimization, which can be widely applied in MSS to find out the set of Pareto-optimal solutions for multiobjective optimization. To deal with multiple objectives and enable the decision maker to make decisions according to different demands on each evaluation index, an analytic hierarchy process is implemented to determine the weight value of evaluation indices. Case study is included to demonstrate the feasibility and robustness of the hybrid algorithm. It is shown from the case study that the multiobjective optimization model can simply, effectively, and objectively select the optimal machining scheme according to the different demands on evaluation indices.

Serving as a guest editor of this special journal issue, I review in this introductory paper some of the important issues concerning applying case-based reasoning (CBR) techniques to the design domain. Through discussions of these issues, I attempt to give the current status of the case-based design field, with surveys of existing case-based design systems and a summary from the first workshop held on this subject. For those who are less familiar with CBR, this paper also contains a section on what CBR method is and how it is contrasted with other reasoning approaches. The last section of this article introduces the papers included in this issue.